Analysis of Head Pose Accuracy in Augmented Reality
IEEE Transactions on Visualization and Computer Graphics
Predicting Accuracy in Pose Estimation for Marker-based Tracking
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
OSGAR: A Scene Graph with Uncertain Transformations
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Ubiquitous Tracking for Augmented Reality
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Accuracy in Optical Tracking with Fiducial Markers: An Accuracy Function for ARToolKit
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
ARTag, a Fiducial Marker System Using Digital Techniques
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Real-Time Camera Tracking Using Known 3D Models and a Particle Filter
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Interactive modelling and tracking for mixed and augmented reality
Proceedings of the ACM symposium on Virtual reality software and technology
Predicting and estimating the accuracy of n-occular optical tracking systems
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
AR-Room: a rapid prototyping framework for augmented reality applications
Multimedia Tools and Applications
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Many Augmented Reality (AR) applications use marker-based vision tracking systems to recover camera pose by detecting one or more planar landmarks. However, most of these systems do not interactively quantify the accuracy of the pose they calculate. Instead, the accuracy of these systems is either ignored, assumed to be a fixed value, or determined using error tables (constructed in an off-line ground-truthed process) along with a run-time interpolation scheme. The validity of these approaches are questionable as errors are strongly dependent on the intrinsic and extrinsic camera parameters and scene geometry. In this paper we present an algorithm for predicting the statistics of marker tracker error in real-time. Based on the Scaled Spherical Simplex Unscented Transform (SSSUT), the algorithm is applied to the Augmented Reality Toolkit Plus (ARToolKitPlus). The results are validated using precision off-line photogrammetric techniques.